Type 2 diabetes and hypertension are common health conditions that often occur together, suggesting shared biological mechanisms. To explore this relationship, we analyse large-scale multiomic data to uncover genetic factors underlying type 2 diabetes and blood pressure comorbidity. We curate 1304 independent single-nucleotide variants associated with type 2 diabetes and blood pressure, grouping them into five clusters related to metabolic syndrome, inverse type 2 diabetes/blood pressure risk, impaired pancreatic beta-cell function, higher adiposity, and vascular dysfunction. Colocalization with tissue-specific gene expression highlights significant enrichment in pathways related to thyroid function and fetal development. Partitioned polygenic scores derived from these clusters improve risk prediction for type 2 diabetes/hypertension comorbidity, identifying individuals with more than twice the usual susceptibility. These results reveal a mechanistically heterogeneous genetic architecture shared between type 2 diabetes and blood pressure, enhancing comorbidity risk prediction. Partitioned polygenic risk scores offer a promising approach for early risk stratification, personalised prevention, and improved management of these interconnected conditions.

Abstract Background Substance use disorders (SUDs), including alcohol and drug dependence, and smoking, pose a public health threat with their high prevalence and comorbidity with other diseases, and contribution to mortality. SUDs are highly correlated, and their genetic background is shared to some degree. Objectives We aimed to investigate the genetic associations of previously reported loci for a wide range of SUDs in an unstudied Ukrainian population. Methods We collected data from 595 individuals (339 women, 253 men), including 321 participants from two rehab centres. Based on clinical review and questionnaire data we defined drug dependence, alcohol dependence, alcohol abuse, binge drinking, smoking, opiate, amphetamine, cannabis, and hallucinogen use, along with several intermediary alcohol use and smoking variables considering the amount of use and the level of dependence. We genotyped COMT -rs4680, ADH1B-ADH1C -rs1789891, and HTR2A -rs6313, and applied logistic and ordered logistic regression assuming an additive inheritance model, controlling for the recruitment group, other substance uses, age, and sex, in the association analyses. Results We replicate ( P <0.05) the associations at COMT -rs4680 with smoking status (OR[95% CI]=1.56[1.01-2.41], P =0.047) and heaviness (1.37[1.04-1.80], P =0.026), and at ADH1B-ADH1C -rs1789891 and HTR2A -rs6313 with alcohol dependence (1.69[1.03-2.76], P =0.038 and 0.66[0.47-0.92, P =0.016], respectively). Furthermore, we provide evidence for an association at HTR2A -rs6313 with hallucinogen use (0.58[0.35-0.98], P =0.040). Conclusion In this study on multiple SUDs we shed light on the genetic background of SUDs in Ukrainians and provide further evidence that variation at COMT is mainly associated with smoking, at ADH1B-ADH1C with alcohol-related variables, whereas HTR2A is a more general SUD-associated locus. Highlights We present the first genetic study of substance use disorders in Ukrainians We replicate the associations at COMT -rs4680 with smoking status and heaviness, and ADH1B-ADH1C -rs1789891 and HTR2A -rs6313 with alcohol dependence We provide evidence for an association at HTR2A -rs6313 with hallucinogen use

Background: Substance use disorders (SUDs), including alcohol and drug dependence, and smoking, pose a public health threat with their high prevalence and comorbidity with other diseases, and contribution to mortality. SUDs are highly correlated, and their genetic background is shared to some degree. Objectives: We aimed to investigate the genetic associations of previously reported loci for a wide range of SUDs in an unstudied Ukrainian population. Methods: We collected data from 595 individuals (339 women, 253 men), including 321 participants from two rehab centres. Based on clinical review and questionnaire data we defined drug dependence, alcohol dependence, alcohol abuse, binge drinking, smoking, opiate, amphetamine, cannabis, and hallucinogen use, along with several intermediary alcohol use and smoking variables considering the amount of use and the level of dependence. We genotyped COMT-rs4680, ADH1B-ADH1C-rs1789891, and HTR2A-rs6313, and applied logistic and ordered logistic regression assuming an additive inheritance model, controlling for the recruitment group, other substance uses, age, and sex, in the association analyses. Results: We replicate (P<0.05) the associations at COMT-rs4680 with smoking status (OR[95% CI]=1.56[1.01-2.41], P=0.047) and heaviness (1.37[1.04-1.80], P=0.026), and at ADH1B-ADH1C-rs1789891 and HTR2A-rs6313 with alcohol dependence (1.69[1.03-2.76], P=0.038 and 0.66[0.47-0.92, P=0.016], respectively). Furthermore, we provide evidence for an association at HTR2A-rs6313 with hallucinogen use (0.58[0.35-0.98], P=0.040). Conclusion: In this study on multiple SUDs we shed light on the genetic background of SUDs in Ukrainians and provide further evidence that variation at COMT is mainly associated with smoking, at ADH1B-ADH1C with alcohol-related variables, whereas HTR2A is a more general SUD-associated locus. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work has been financially supported by University of Surrey Faculty Research Support Fund, US-Ukraine Biotech Initiative Small Research Grant, Crowd.Science. The work has been partiallly carried out within the initiative research project IN.14.24/ IN.14.25 (National Academy of Medical Sciences of Ukraine) "Study of genetic predisposition to the substance use disorders (SUDs) among the population of Ukraine". ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of Institute of Gerontology, National Academy of Medical Sciences of Ukraine gave ethical approval for this work (Protocol N 8 From 14 August 2020). Ethics committee of University of Surrey, UK gave ethical approval for this work (EGA ref: FHMS 20-21 198 EGA, approved on 7 October 2021). Ethics committee of O.M. Marzieiev Institute for Public Health, National Academy of Medical Sciences of Ukraine gave ethical approval for this work (Protocol N 1 from 11.06.2024) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The individual-level data that support the findings of this study are not openly available due to reasons of sensitivity and potential harm to the participants in case of data leakages or accidental identification. Some of the data may be available from the corresponding author upon a reasonable request. Data are located in a controlled access data storage.

Type 2 diabetes (T2D) and hypertension are common health conditions that often occur together, suggesting shared biological mechanisms. To explore this relationship, we analysed large-scale multiomic data to uncover genetic factors underlying T2D and blood pressure (BP) comorbidity. We curated 1,304 independent single-nucleotide variants (SNVs) associated with T2D/BP, grouping them into five clusters related to metabolic syndrome, inverse T2D-BP risk, impaired pancreatic beta-cell function, higher adiposity, and vascular dysfunction. Colocalisation with tissue-specific gene expression highlighted significant enrichment in pathways related to thyroid function and fetal development. Partitioned polygenic scores (PGS) derived from these clusters improved risk prediction for T2D-hypertension comorbidity, identifying individuals with more than twice usual susceptibility. These results reveal complex genetic basis of shared T2D and BP mechanistic heterogeneity, enhancing comorbidity risk prediction. Partitioned PGSs offer promising approach for early risk stratification, personalised prevention, and improved management of these interconnected conditions, supporting precision medicine and public health initiatives. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research has been conducted using the UK Biobank Resource under application number 236. This project was in part funded by the Agence Nationale de la Recherche under the Programme d'Investissement d'Avenir (PreciDIAB, ANR-18-IBHU-0001 and RHU PreciNASH ANR-16-RHUS-0006), by the European Union through the "Fonds Europeen de Developpement Regional" (FEDER), by the "Conseil Regional des Hauts-de-France" (Hauts-de-France Regional Council), by the "Metropole Europeenne de Lille" (MEL, European Metropolis of Lille), and by the European Research Council (ERC OpiO - 101043671, to AB) The authors would like to thank all the investigators from different consortia that built and shared the GWAS meta-analysis, eQTLs, and scATAC-seq atlases used in this study, as well as the UK Biobank participants and dedicated staff. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The GWAS used in this study are all publicly available and listed in Supplementary Table 4. The UK Biobank Resource (UKB, https://ukbiobank.ac.uk/) was accessed using the Application Number 236. GTEx (https://www.gtexportal.org/home/datasets/) and TIGER (http://tiger.bsc.es/) eQTLs are publicly available. The ATAC-seq data from CATLAS are publicly available http://catlas.org/humanenhancer/. Data from the ABOS cohort are not publicly available, as the study is ongoing. The Biological Atlas of Severe Obesity (Atlas Biologique de l'Obesité Sévère [ABOS]) cohort (ClinicalTrials.gov: [NCT01129297][1]) is an ongoing prospective study that aims to identify the determinants of bariatric surgery outcomes. Patients were recruited at the Centre Hospitalier Universitaire de Lille (France), as previously described in DOI: 10.1097/SLA.0000000000000945, DOI: 10.1016/S2213-8587(22)00005-5, and DOI: 10.1038/s41467-024-51078-2. All human procedures were ethically approved by the Comité de Protection des Personnes Nord Ouest IV or by the ethics committee of Liège University Hospital. The analysis performed in this study aligned with the original scopes and objectives of the ABOS and Liège cohort studies; therefore, no additional ethical approval was requested. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The GWAS used in this study are all publicly available and listed in Supplementary Table 4. The UK Biobank Resource (UKB, https://ukbiobank.ac.uk/) was accessed using the Application Number 236. GTEx (https://www.gtexportal.org/home/datasets/) and TIGER (http://tiger.bsc.es/) eQTLs are publicly available. Data from the ABOS cohort are not publicly available, as the study is ongoing. The ATAC-seq data from CATLAS are publicly available http://catlas.org/humanenhancer/. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01129297&atom=%2Fmedrxiv%2Fearly%2F2025%2F03%2F06%2F2025.03.02.25323190.atom

We have developed a novel Bayesian Linear Structural Equations Model (BLSEM) with variable selection priors (available as an R package) to build directed acyclic graphs to delineate complex variable associations and pathways to BMI development. Conditional on standard assumptions used in causal inference, the model provides interpretable estimates with uncertainty for natural direct, indirect (mediated) and total effects. We showcase our method using data on 4119 offspring followed from the pre-pregnancy period to age 46 years (y) in a Finnish population-based birth cohort. The BLSEM enabled efficiently to analyse all available data over the long-time span, identifying factors to distil potential causal pathways contributing to adult BMI development. All of the associations between early childhood and adolescence variables with adult BMI at 46 y (BMI46) were indirect via multiple paths. For example, maternal prepregnancy BMI, smoking and socioeconomic position are associated with BMI46 through 35, 31 and 26 paths. Another notable feature was that the contribution of very early life factors, particularly prenatal, was captured by growth patterns along childhood, which were the strongest early predictors of middle age BMI46 (the age at adiposity rebound (AgeAR), early growth parameters between the AgeAR to 11 y). BMI and blood pressure measured 15 y earlier also predicted BMI46, all other factors held constant. Genetic predisposition by the polygenic risk score for BMI showed an indirect effect that became apparent at AgeAR and thereafter. The Bayesian approach we present and the BLSEM software developed advances methodologies for the analysis of complex, multifaceted life-course data prior to the estimation of potential causal pathways. Our results, although exploratory in nature, suggest that the effective interventions to tackle adverse BMI development could be designed throughout childhood, though the period by AgeAR may be paramount. We feature the importance of integrated life-course analyses that help to understand the contribution of life-stage factors of development.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in GWAS. Using childhood BMI as an example trait, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS of the 12 estimated phenotypes identified 28 genome-wide significant variants at 13 loci, one of which (in DAOA) has not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover unique biological mechanisms influencing quantitative traits. This article presents a framework to conduct GWAS of longitudinal data where the trait of interest follows a non-linear change over time. The framework is applied to childhood BMI, identifying 13 loci with age-varying genetic effects.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS on each of the estimated phenotypes identified 28 genome-wide significant variants at 13 loci across the 12 estimated phenotypes, one of which was novel (in DAOA) and had not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover novel biological mechanisms influencing quantitative traits.

Introduction Polygenic Score (PGS) is a valuable method for assessing the estimated genetic liability to a given outcome or genetic variability contributing to a quantitative trait. While PRSs are widely used for complex traits, their application in uncovering shared genetic predisposition between phenotypes, i.e. when genetic variants influence more than one phenotype, remains limited. Methods We developed an R package, comorbidPGS, which facilitates a systematic evaluation of shared genetic effects among (cor)related phenotypes using PGSs. The comorbidPGS package takes as input a set of Single Nucleotide Polymorphisms (SNPs) along with their established effects on the original phenotype (Po), referred to as Po-PGS. It generates a comprehensive summary of effect(s) of Po-PGS on target phenotype(s) (Pt) with customisable graphical features. Results We applied comorbidPGS to investigate the shared genetic predisposition between phenotypes defining elevated blood pressure (Systolic Blood Pressure, SBP; Diastolic Blood Pressure, DBP; Pulse Pressure, PP) and several cancers (Breast Cancer, BrC; Pancreatic Cancer, PanC; Kidney Cancer, KidC; Prostate Cancer, PrC; Colorectal Cancer, CrC) using the European ancestry UK Biobank individuals and GWAS meta-analyses summary statistics from independent set of European ancestry individuals. We report a significant association between elevated DBP and the genetic risk of PrC (β (SE)=0.066 (0.017), P-value=9.64×10^(-5)), as well as between CrC PGS and both, lower SBP (β (SE)=-0.10 [0.029], P-value=3.83×10^(-4))) and lower DBP (β (SE)=-0.055 [0.017], P-value=1.05×10^(-3)). Our analysis highlights two nominally significant relationships for individuals with genetic predisposition to elevated SBP leading to higher risk of KidC (OR [95%CI]=1.04 [1.0039-1.087], P-value=2.82×10^(-2)) and PrC (OR [95%CI]=1.02 [1.003-1.041], P-value=2.22×10^(-2)). Conclusion Using comorbidPGS, we underscore mechanistic relationships between blood pressure regulation and susceptibility to three comorbid malignancies. This package offers valuable means to evaluate shared genetic susceptibility between (cor)related phenotypes through polygenic scores.

Background Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank. Results Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation. Conclusion We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single “optimal” pubertal growth pattern.

ABSTRACT OBJECTIVE Diverse measures of obesity relate to cancer risk differently. Here we assess the relationship between overall and central adiposity and cancer. METHODS We constructed z-score weighted polygenic scores (PGS) for two obesity-related phenotypes; body mass index (BMI) and BMI adjusted waist-to-hip ratio (WHRadjBMI) and tested for their association with five cancers in the UK Biobank: overall breast (BrC), post-menopausal breast (PostBrC), prostate (PrC), colorectal (CrC) and lung (LungC) cancer. We utilised publicly available data to perform bi-directional Mendelian randomization (MR) between BMI/WHRadjBMI and BrC, PrC and CrC. RESULTS PGS BMI had significant multiple testing-corrected inverse association with PrC (OR[95%CI]=0.97[0.95-0.99], P =0.0012) but PGS WHRadjBMI was not associated with PrC. PGS BMI was associated with PostBrC (OR[95%CI]=0.97[0.96-0.99], P =0.00203) while PGS WHRadjBMI had nominal association with BrC. PGS BMI had nominal positive association with LungC. MR analyses showed significant multiple testing-corrected protective causal effect of BMI on PrC (OR[95%CI]=0.993[0.988-0.998], P =4.19×10 −3 ). WHRadjBMI had a nominal causal effect on higher PrC risk (OR[95%CI]=1.022[1.0067-1.038], P =0.0053). We also report nominal causal protective effect of WHRadjBMI on breast cancer (OR[95%CI]=0.99[0.98-0.997], P =0.0068). Neither PGS nor MR analyses were significant for CrC. CONCLUSIONS Higher overall adiposity appears protective from PrC while higher central adiposity is a potential risk factor for PrC but protective from BrC. STUDY IMPORTANCE What is already known about this subject? Observational studies suggest obesity is associated with higher risk of certain cancers and at the same time is protective of other cancers. The direction of association is in part influenced by the anthropometric trait used to assess obesity. Higher BMI appears protective from prostate, breast and lung cancers but is a risk factor for post-menopausal breast, pancreatic and colorectal cancers. What are the new findings in your manuscript? We implement Mendelian randomization approach using large scale datasets and show a protective causal effect of higher BMI from prostate cancer but suggest that higher WHRadjBMI is causal for prostate cancer. We also show nominal evidence of WHRadjBMI being causally protective from breast cancer. How might your results change the direction of research or the focus of clinical practice? We demonstrate the importance of partitioning obesity into discrete types depending on the area of fat deposition rather than using an overall measure. Our results show that diverse measures of obesity relate differently to cancer risk. In fact, even for the same type of cancer, overall and central obesity measures may impact in opposite direction in terms of risk to cancer.